Overview

Dataset statistics

Number of variables12
Number of observations1,000
Missing cells0
Missing cells (%)0.0%
Duplicate rows360
Duplicate rows (%)36.0%
Total size in memory315.6 KiB
Average record size in memory323.1 B

Variable types

Numeric8
Categorical3
Boolean1

Alerts

Dataset has 360 (36.0%) duplicate rowsDuplicates
5G Capability is highly overall correlated with Market Share (%) and 1 other fieldsHigh correlation
Market Share (%) is highly overall correlated with 5G CapabilityHigh correlation
Product Model is highly overall correlated with 5G CapabilityHigh correlation

Reproduction

Analysis started2026-02-19 11:46:36.262441
Analysis finished2026-02-19 11:46:48.745936
Duration12.48 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Year
Real number (ℝ)

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021.456
Minimum2019
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-19T17:16:49.057083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2019
5-th percentile2019
Q12020
median2021
Q32023
95-th percentile2024
Maximum2024
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7002808
Coefficient of variation (CV)0.00084111692
Kurtosis-1.2533538
Mean2021.456
Median Absolute Deviation (MAD)1
Skewness0.025365695
Sum2021456
Variance2.890955
MonotonicityNot monotonic
2026-02-19T17:16:49.232662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2019173
17.3%
2021171
17.1%
2022169
16.9%
2020166
16.6%
2023164
16.4%
2024157
15.7%
ValueCountFrequency (%)
2019173
17.3%
2020166
16.6%
2021171
17.1%
2022169
16.9%
2023164
16.4%
2024157
15.7%
ValueCountFrequency (%)
2024157
15.7%
2023164
16.4%
2022169
16.9%
2021171
17.1%
2020166
16.6%
2019173
17.3%

Quarter
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
Q1
256 
Q2
255 
Q3
248 
Q4
241 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2,000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ1
2nd rowQ1
3rd rowQ1
4th rowQ1
5th rowQ1

Common Values

ValueCountFrequency (%)
Q1256
25.6%
Q2255
25.5%
Q3248
24.8%
Q4241
24.1%

Length

2026-02-19T17:16:49.426213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-19T17:16:49.603788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
q1256
25.6%
q2255
25.5%
q3248
24.8%
q4241
24.1%

Most occurring characters

ValueCountFrequency (%)
Q1000
50.0%
1256
 
12.8%
2255
 
12.8%
3248
 
12.4%
4241
 
12.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Q1000
50.0%
1256
 
12.8%
2255
 
12.8%
3248
 
12.4%
4241
 
12.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Q1000
50.0%
1256
 
12.8%
2255
 
12.8%
3248
 
12.4%
4241
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Q1000
50.0%
1256
 
12.8%
2255
 
12.8%
3248
 
12.4%
4241
 
12.0%

Product Model
Categorical

High correlation 

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size69.0 KiB
Galaxy S22 5G
79 
Galaxy Note10
74 
Galaxy A32 5G
69 
Galaxy Z Flip3 5G
69 
Galaxy Z Fold3 5G
69 
Other values (10)
640 

Length

Max length17
Median length13
Mean length13.477
Min length10

Characters and Unicode

Total characters13,477
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGalaxy S10
2nd rowGalaxy Note10
3rd rowGalaxy S20
4th rowGalaxy Note20
5th rowGalaxy S21

Common Values

ValueCountFrequency (%)
Galaxy S22 5G79
 
7.9%
Galaxy Note1074
 
7.4%
Galaxy A32 5G69
 
6.9%
Galaxy Z Flip3 5G69
 
6.9%
Galaxy Z Fold3 5G69
 
6.9%
Galaxy A52 5G68
 
6.8%
Galaxy Note2067
 
6.7%
Galaxy S2167
 
6.7%
Galaxy S1066
 
6.6%
Galaxy A73 5G66
 
6.6%
Other values (5)306
30.6%

Length

2026-02-19T17:16:49.867095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
galaxy1000
34.1%
5g666
22.7%
z264
 
9.0%
s2279
 
2.7%
note1074
 
2.5%
a3269
 
2.4%
flip369
 
2.4%
fold369
 
2.4%
a5268
 
2.3%
s2167
 
2.3%
Other values (8)505
17.2%

Most occurring characters

ValueCountFrequency (%)
a2000
14.8%
1930
14.3%
G1666
12.4%
l1264
9.4%
x1000
 
7.4%
y1000
 
7.4%
5796
 
5.9%
2611
 
4.5%
3331
 
2.5%
S330
 
2.4%
Other values (14)2549
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)13477
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a2000
14.8%
1930
14.3%
G1666
12.4%
l1264
9.4%
x1000
 
7.4%
y1000
 
7.4%
5796
 
5.9%
2611
 
4.5%
3331
 
2.5%
S330
 
2.4%
Other values (14)2549
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13477
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a2000
14.8%
1930
14.3%
G1666
12.4%
l1264
9.4%
x1000
 
7.4%
y1000
 
7.4%
5796
 
5.9%
2611
 
4.5%
3331
 
2.5%
S330
 
2.4%
Other values (14)2549
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13477
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a2000
14.8%
1930
14.3%
G1666
12.4%
l1264
9.4%
x1000
 
7.4%
y1000
 
7.4%
5796
 
5.9%
2611
 
4.5%
3331
 
2.5%
S330
 
2.4%
Other values (14)2549
18.9%

5G Capability
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
666 
False
334 
ValueCountFrequency (%)
True666
66.6%
False334
33.4%
2026-02-19T17:16:50.116087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Units Sold
Real number (ℝ)

Distinct354
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32842.99
Minimum5309
Maximum64883
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-19T17:16:50.283508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5309
5-th percentile7473
Q119327.25
median33689
Q343911
95-th percentile58340.25
Maximum64883
Range59574
Interquartile range (IQR)24583.75

Descriptive statistics

Standard deviation16039.771
Coefficient of variation (CV)0.48837729
Kurtosis-0.95610633
Mean32842.99
Median Absolute Deviation (MAD)12477
Skewness0.040410034
Sum32842990
Variance2.5727424 × 108
MonotonicityNot monotonic
2026-02-19T17:16:50.493279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
381196
 
0.6%
54656
 
0.6%
422666
 
0.6%
362556
 
0.6%
266345
 
0.5%
428205
 
0.5%
150075
 
0.5%
404185
 
0.5%
83305
 
0.5%
362165
 
0.5%
Other values (344)946
94.6%
ValueCountFrequency (%)
53092
 
0.2%
54656
0.6%
55642
 
0.2%
57182
 
0.2%
65012
 
0.2%
65423
0.3%
65483
0.3%
65572
 
0.2%
66284
0.4%
67193
0.3%
ValueCountFrequency (%)
648833
0.3%
643812
0.2%
639743
0.3%
639663
0.3%
638042
0.2%
634942
0.2%
631374
0.4%
629683
0.3%
622152
0.2%
619582
0.2%

Revenue ($)
Real number (ℝ)

Distinct360
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30197332
Minimum2987436.4
Maximum84264944
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-19T17:16:50.708670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2987436.4
5-th percentile6397386.2
Q114607494
median28012005
Q341803911
95-th percentile65444721
Maximum84264944
Range81277507
Interquartile range (IQR)27196417

Descriptive statistics

Standard deviation18379406
Coefficient of variation (CV)0.60864339
Kurtosis-0.21298281
Mean30197332
Median Absolute Deviation (MAD)13615176
Skewness0.68631469
Sum3.0197332 × 1010
Variance3.3780258 × 1014
MonotonicityNot monotonic
2026-02-19T17:16:50.952060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14185149.886
 
0.6%
49899540.86
 
0.6%
12091673.946
 
0.6%
35842354.885
 
0.5%
33782555.285
 
0.5%
63070767.795
 
0.5%
45919818.685
 
0.5%
56176071.665
 
0.5%
17977115.155
 
0.5%
19621770.615
 
0.5%
Other values (350)947
94.7%
ValueCountFrequency (%)
2987436.3842
0.2%
4212951.0483
0.3%
4252116.8882
0.2%
4424999.4992
0.2%
4494602.4933
0.3%
4828393.0624
0.4%
4874859.7383
0.3%
4967036.6162
0.2%
5030391.842
0.2%
5136019.5424
0.4%
ValueCountFrequency (%)
84264943.842
0.2%
82025137.22
0.2%
80946999.253
0.3%
79592293.163
0.3%
79207664.344
0.4%
78970161.312
0.2%
76740547.762
0.2%
75369161.282
0.2%
71988709.972
0.2%
71564619.923
0.3%

Market Share (%)
Real number (ℝ)

High correlation 

Distinct274
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.72357
Minimum-0.49
Maximum6.95
Zeros2
Zeros (%)0.2%
Negative49
Negative (%)4.9%
Memory size7.9 KiB
2026-02-19T17:16:51.194512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.49
5-th percentile0
Q12.635
median3.76
Q35.2825
95-th percentile6.65
Maximum6.95
Range7.44
Interquartile range (IQR)2.6475

Descriptive statistics

Standard deviation1.9911085
Coefficient of variation (CV)0.53473104
Kurtosis-0.7364446
Mean3.72357
Median Absolute Deviation (MAD)1.31
Skewness-0.31978569
Sum3723.57
Variance3.9645129
MonotonicityNot monotonic
2026-02-19T17:16:51.451480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.0213
 
1.3%
0.3413
 
1.3%
3.8812
 
1.2%
4.2112
 
1.2%
3.4411
 
1.1%
6.3311
 
1.1%
4.669
 
0.9%
5.288
 
0.8%
6.18
 
0.8%
3.848
 
0.8%
Other values (264)895
89.5%
ValueCountFrequency (%)
-0.492
 
0.2%
-0.473
0.3%
-0.422
 
0.2%
-0.392
 
0.2%
-0.363
0.3%
-0.352
 
0.2%
-0.255
0.5%
-0.233
0.3%
-0.24
0.4%
-0.172
 
0.2%
ValueCountFrequency (%)
6.953
0.3%
6.924
0.4%
6.915
0.5%
6.846
0.6%
6.837
0.7%
6.823
0.3%
6.815
0.5%
6.753
0.3%
6.716
0.6%
6.72
 
0.2%

Regional 5G Coverage (%)
Real number (ℝ)

Distinct344
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.88972
Minimum25.34
Maximum103.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-19T17:16:51.666877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum25.34
5-th percentile38.79
Q150.4
median67.05
Q383.21
95-th percentile98.2245
Maximum103.92
Range78.58
Interquartile range (IQR)32.81

Descriptive statistics

Standard deviation19.254095
Coefficient of variation (CV)0.28784834
Kurtosis-0.95536163
Mean66.88972
Median Absolute Deviation (MAD)16.42
Skewness0.018700315
Sum66889.72
Variance370.72016
MonotonicityNot monotonic
2026-02-19T17:16:51.898296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.9610
 
1.0%
69.328
 
0.8%
33.548
 
0.8%
87.837
 
0.7%
74.467
 
0.7%
48.757
 
0.7%
73.546
 
0.6%
75.226
 
0.6%
47.846
 
0.6%
676
 
0.6%
Other values (334)929
92.9%
ValueCountFrequency (%)
25.342
0.2%
25.73
0.3%
25.883
0.3%
27.273
0.3%
27.943
0.3%
28.493
0.3%
30.324
0.4%
30.373
0.3%
31.642
0.2%
32.092
0.2%
ValueCountFrequency (%)
103.922
0.2%
103.822
0.2%
103.783
0.3%
103.733
0.3%
103.573
0.3%
102.82
0.2%
102.542
0.2%
102.492
0.2%
102.382
0.2%
101.83
0.3%

5G Subscribers (millions)
Real number (ℝ)

Distinct350
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.15208
Minimum-0.89
Maximum54.94
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)1.0%
Memory size7.9 KiB
2026-02-19T17:16:52.100906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.89
5-th percentile6.712
Q118.4125
median29.915
Q344.36
95-th percentile51.8
Maximum54.94
Range55.83
Interquartile range (IQR)25.9475

Descriptive statistics

Standard deviation14.537781
Coefficient of variation (CV)0.48214853
Kurtosis-1.0357426
Mean30.15208
Median Absolute Deviation (MAD)13.01
Skewness-0.1497599
Sum30152.08
Variance211.34708
MonotonicityNot monotonic
2026-02-19T17:16:52.305402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.969
 
0.9%
23.967
 
0.7%
48.177
 
0.7%
13.996
 
0.6%
45.996
 
0.6%
8.116
 
0.6%
38.336
 
0.6%
9.776
 
0.6%
13.225
 
0.5%
24.695
 
0.5%
Other values (340)937
93.7%
ValueCountFrequency (%)
-0.892
 
0.2%
-0.654
0.4%
-0.054
0.4%
0.343
0.3%
0.842
 
0.2%
1.473
0.3%
1.573
0.3%
1.683
0.3%
2.272
 
0.2%
2.535
0.5%
ValueCountFrequency (%)
54.943
0.3%
54.482
0.2%
54.352
0.2%
54.214
0.4%
53.762
0.2%
53.482
0.2%
53.42
0.2%
53.262
0.2%
52.822
0.2%
52.812
0.2%

Avg 5G Speed (Mbps)
Real number (ℝ)

Distinct358
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.22556
Minimum50.37
Maximum298.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-19T17:16:52.515335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum50.37
5-th percentile67.46
Q1120.41
median177.39
Q3238.86
95-th percentile287.88
Maximum298.7
Range248.33
Interquartile range (IQR)118.45

Descriptive statistics

Standard deviation70.470934
Coefficient of variation (CV)0.39319689
Kurtosis-1.1146455
Mean179.22556
Median Absolute Deviation (MAD)59.22
Skewness-0.018571924
Sum179225.56
Variance4966.1525
MonotonicityNot monotonic
2026-02-19T17:16:52.731249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
292.186
 
0.6%
284.336
 
0.6%
81.436
 
0.6%
238.865
 
0.5%
162.285
 
0.5%
160.985
 
0.5%
85.445
 
0.5%
175.65
 
0.5%
177.435
 
0.5%
66.345
 
0.5%
Other values (348)947
94.7%
ValueCountFrequency (%)
50.372
0.2%
51.122
0.2%
51.733
0.3%
52.282
0.2%
52.523
0.3%
52.853
0.3%
54.562
0.2%
57.252
0.2%
57.273
0.3%
58.212
0.2%
ValueCountFrequency (%)
298.72
0.2%
297.812
0.2%
297.32
0.2%
296.752
0.2%
296.122
0.2%
295.852
0.2%
295.622
0.2%
294.913
0.3%
294.863
0.3%
294.813
0.3%

Preference for 5G (%)
Real number (ℝ)

Distinct347
Distinct (%)34.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.14291
Minimum37.14
Maximum94.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-19T17:16:52.945767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum37.14
5-th percentile43.46
Q153.2675
median66.96
Q380.99
95-th percentile91.7235
Maximum94.84
Range57.7
Interquartile range (IQR)27.7225

Descriptive statistics

Standard deviation15.75925
Coefficient of variation (CV)0.23471206
Kurtosis-1.208542
Mean67.14291
Median Absolute Deviation (MAD)14
Skewness-0.00061770557
Sum67142.91
Variance248.35398
MonotonicityNot monotonic
2026-02-19T17:16:53.154428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63.047
 
0.7%
88.167
 
0.7%
87.016
 
0.6%
84.086
 
0.6%
86.826
 
0.6%
37.146
 
0.6%
85.886
 
0.6%
63.865
 
0.5%
59.65
 
0.5%
75.515
 
0.5%
Other values (337)941
94.1%
ValueCountFrequency (%)
37.146
0.6%
37.265
0.5%
38.072
 
0.2%
38.283
0.3%
38.52
 
0.2%
39.334
0.4%
39.572
 
0.2%
40.033
0.3%
40.122
 
0.2%
41.312
 
0.2%
ValueCountFrequency (%)
94.842
0.2%
94.72
0.2%
94.434
0.4%
94.363
0.3%
94.163
0.3%
93.454
0.4%
93.383
0.3%
93.283
0.3%
93.252
0.2%
93.23
0.3%

Region
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size68.4 KiB
North America
226 
Latin America
205 
Middle East & Africa
200 
Europe
192 
Asia-Pacific
177 

Length

Max length20
Median length13
Mean length12.879
Min length6

Characters and Unicode

Total characters12,879
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsia-Pacific
2nd rowLatin America
3rd rowMiddle East & Africa
4th rowNorth America
5th rowLatin America

Common Values

ValueCountFrequency (%)
North America226
22.6%
Latin America205
20.5%
Middle East & Africa200
20.0%
Europe192
19.2%
Asia-Pacific177
17.7%

Length

2026-02-19T17:16:53.398367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-19T17:16:53.564089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
america431
21.2%
north226
11.1%
latin205
10.1%
middle200
9.8%
east200
9.8%
200
9.8%
africa200
9.8%
europe192
9.5%
asia-pacific177
8.7%

Most occurring characters

ValueCountFrequency (%)
i1567
12.2%
a1390
 
10.8%
r1049
 
8.1%
1031
 
8.0%
c985
 
7.6%
e823
 
6.4%
A808
 
6.3%
t631
 
4.9%
m431
 
3.3%
o418
 
3.2%
Other values (15)3746
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)12879
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i1567
12.2%
a1390
 
10.8%
r1049
 
8.1%
1031
 
8.0%
c985
 
7.6%
e823
 
6.4%
A808
 
6.3%
t631
 
4.9%
m431
 
3.3%
o418
 
3.2%
Other values (15)3746
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12879
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i1567
12.2%
a1390
 
10.8%
r1049
 
8.1%
1031
 
8.0%
c985
 
7.6%
e823
 
6.4%
A808
 
6.3%
t631
 
4.9%
m431
 
3.3%
o418
 
3.2%
Other values (15)3746
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12879
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i1567
12.2%
a1390
 
10.8%
r1049
 
8.1%
1031
 
8.0%
c985
 
7.6%
e823
 
6.4%
A808
 
6.3%
t631
 
4.9%
m431
 
3.3%
o418
 
3.2%
Other values (15)3746
29.1%

Interactions

2026-02-19T17:16:46.986725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:37.186248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:38.556610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:39.932949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:41.459056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:42.735478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:44.099405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:45.479312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:47.141723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:37.340117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:38.707595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:40.092853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:41.607033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:42.909588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:44.247326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:45.623873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:47.298750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:37.492459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:38.866125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:40.249081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:41.766690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:43.067837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:44.405770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:45.785171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:47.469604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:37.654001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:39.040198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:40.425622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:41.935299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:43.243775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:44.610105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:45.957361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:47.626768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:37.818581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:39.240508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:40.792887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:42.095890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:43.410836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:44.785146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:46.115020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:47.793972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:37.999608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:39.458938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:40.966904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:42.262343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:43.605054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:44.954067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:46.282505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:47.945395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:38.223874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:39.616314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:41.132058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:42.424335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:43.769834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:45.153492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:46.435118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:48.093181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:38.383878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:39.779304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:41.295604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:42.583813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:43.935376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:45.315121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2026-02-19T17:16:46.586492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2026-02-19T17:16:53.707355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
5G Capability5G Subscribers (millions)Avg 5G Speed (Mbps)Market Share (%)Preference for 5G (%)Product ModelQuarterRegionRegional 5G Coverage (%)Revenue ($)Units SoldYear
5G Capability1.0000.3620.0890.7980.3960.9930.0000.0540.4120.2870.3480.000
5G Subscribers (millions)0.3621.0000.0960.1720.0450.2230.1460.1260.1390.0830.091-0.023
Avg 5G Speed (Mbps)0.0890.0961.0000.009-0.0290.1660.1040.1190.0460.1310.0300.131
Market Share (%)0.7980.1720.0091.0000.2460.2900.0900.1470.1760.1600.1200.019
Preference for 5G (%)0.3960.045-0.0290.2461.0000.2400.1050.1330.0200.1610.060-0.071
Product Model0.9930.2230.1660.2900.2401.0000.0000.1700.2250.1830.2140.000
Quarter0.0000.1460.1040.0900.1050.0001.0000.0730.1270.1100.1510.000
Region0.0540.1260.1190.1470.1330.1700.0731.0000.1150.1510.1350.059
Regional 5G Coverage (%)0.4120.1390.0460.1760.0200.2250.1270.1151.0000.0950.0910.069
Revenue ($)0.2870.0830.1310.1600.1610.1830.1100.1510.0951.0000.051-0.044
Units Sold0.3480.0910.0300.1200.0600.2140.1510.1350.0910.0511.0000.000
Year0.000-0.0230.1310.019-0.0710.0000.0000.0590.069-0.0440.0001.000

Missing values

2026-02-19T17:16:48.295867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-19T17:16:48.584745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

YearQuarterProduct Model5G CapabilityUnits SoldRevenue ($)Market Share (%)Regional 5G Coverage (%)5G Subscribers (millions)Avg 5G Speed (Mbps)Preference for 5G (%)Region
02019Q1Galaxy S10No263964.212951e+061.0457.3639.55293.1055.87Asia-Pacific
12019Q1Galaxy Note10No256717.240266e+062.8285.8042.5867.4637.26Latin America
22019Q1Galaxy S20No165732.560833e+07-0.0347.023.7877.2584.66Middle East & Africa
32019Q1Galaxy Note20No71772.198442e+070.8425.7023.41105.2740.03North America
42019Q1Galaxy S21No456331.634244e+072.3689.1344.43206.1776.88Latin America
52019Q1Galaxy A32 5GYes159121.717833e+075.4159.1212.14179.1580.79Middle East & Africa
62019Q1Galaxy A52 5GYes72314.863981e+075.9052.9444.36259.1677.55Latin America
72019Q1Galaxy A73 5GYes587117.017578e+074.9279.9549.70191.4274.83Europe
82019Q1Galaxy Z Fold2 5GYes406415.195956e+072.6444.7731.27151.1866.51North America
92019Q1Galaxy Z Flip3 5GYes381194.989954e+076.8451.2138.33284.3387.01Latin America
YearQuarterProduct Model5G CapabilityUnits SoldRevenue ($)Market Share (%)Regional 5G Coverage (%)5G Subscribers (millions)Avg 5G Speed (Mbps)Preference for 5G (%)Region
9902020Q1Galaxy A32 5GYes428831.858873e+076.3772.8745.86273.5952.90Asia-Pacific
9912023Q1Galaxy Z Flip5 5GYes116581.413069e+074.8164.1550.51117.9262.56Europe
9922020Q1Galaxy S22 5GYes408072.840947e+073.0447.5633.5995.3857.91Latin America
9932019Q4Galaxy Z Flip3 5GYes345412.330878e+074.0359.358.37138.6780.35Latin America
9942019Q4Galaxy S22 5GYes67193.831129e+075.3842.5550.32110.9787.80Asia-Pacific
9952023Q4Galaxy S22 5GYes362162.995937e+073.8270.5946.92177.4363.86Latin America
9962022Q2Galaxy S21No338062.369938e+07-0.2377.3147.51129.7078.41North America
9972022Q1Galaxy S10No236782.330203e+070.5845.6143.79156.5672.06Europe
9982023Q4Galaxy Note10No356971.946256e+072.4936.5536.44236.3947.11North America
9992020Q4Galaxy Note20No74731.962177e+073.8874.6627.55177.2272.36North America

Duplicate rows

Most frequently occurring

YearQuarterProduct Model5G CapabilityUnits SoldRevenue ($)Market Share (%)Regional 5G Coverage (%)5G Subscribers (millions)Avg 5G Speed (Mbps)Preference for 5G (%)Region# duplicates
92019Q1Galaxy S22 5GYes362551.209167e+074.6656.509.7781.4385.88Middle East & Africa6
112019Q1Galaxy Z Flip3 5GYes381194.989954e+076.8451.2138.33284.3387.01Latin America6
1992022Q2Galaxy Note10No54651.418515e+071.9773.5445.99292.1837.14North America6
42019Q1Galaxy Note10No256717.240266e+062.8285.8042.5867.4637.26Latin America5
252019Q2Galaxy S23 5GYes366136.307077e+073.2583.9613.22280.7375.51North America5
1102020Q4Galaxy Note20No74731.962177e+073.8874.6627.55177.2272.36North America5
1252021Q1Galaxy Note20No364769.769649e+060.4067.0529.96138.1252.54Europe5
1352021Q2Galaxy A14 5GYes335761.575834e+076.6573.3714.8569.6558.52Europe5
1362021Q2Galaxy A32 5GYes266341.797712e+072.9745.9352.08238.8674.08Middle East & Africa5
1562021Q3Galaxy S10No390311.860894e+073.8133.5415.30107.9858.30Asia-Pacific5